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Using LLMs at Oxide

rfd.shared.oxide.computer

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Re: Using LLMs at Oxide

#211

Post by rafterydj »

IshKebab wrote: post #187
don-bright wrote: post #120

Earlier quoted context omitted.

Also since LLM generated content is not copyrightable what happens to code you publish as Copyleft license? The entire copyleft system is based on the idea of a human holding copyright to copyleft code. Is a big chunk of it, the LLM part, basically public domain? How do you ensure theres enough human content to make it copyrightable and hence copyleftable….

> since LLM generated content is not copyrightable That's not how it works. If you ask an LLM to write Harry Potter and it writes something that is 99% the same as Harry Potter, it isn't magically free of copyright. That would obviously be insane. The legal system is still figuring out exactly what the rules are here but it seems likely that it's going to be on the LLM user to know if the output is protected by copyr…

>it seems likely that it's going to be on the LLM user to know if the out is protected by copyright.

To me, this is what seems more insane! If you've never read Harry Potter, and you ask an LLM to write you a story about a wizard boy, and it outputs 80% Harry Potter - how would you even know?

> there will be probably be some "reasonable belief" defence in eventual laws.

This is probably true, but it's irksome to shift all blame away from the LLM producers, using copy-written data to peddle copy-written output. This simply turns the business into copyright infringement as a service - what incentive would they have to actually build those "secondary search thingies" and build them well?

> it definitely isn't as simple as "LLM wrote it so we can ignore copyright".

Agreed. The copyright system is getting stress tested. It will be interesting to see how our legal systems can adapt to this.

Re: Using LLMs at Oxide

#212

Post by pxc »

bigiain wrote: post #47
danenania wrote: post #41

Earlier quoted context omitted.

A problem I’ve found with LLMs for docs is that they are like ten times too wordy. They want to document every path and edge case rather focusing on what really matters. It can be addressed with prompting, but you have to fight this constantly.

I think probably my most common prompt is "Make it shorter. No more than ($x) (words|sentences|paragraphs)."

I've never been able to get that to work. LLMs can't count; they don't actually know how long their output is.

Re: Using LLMs at Oxide

#213

Post by Jaygles »

zeroonetwothree wrote: post #104
Jaygles wrote: post #79

Earlier quoted context omitted.

I exclusively use the autocomplete in cursor. I hate reviewing huge chunks of llm code at one time. With the autocomplete, I’m in full control of the larger design and am able to quickly review each piece of llm code. Very often it generates what I was going to type myself. Anything that involves math or complicated conditions I take extra time on. I feel I’m getting code written 2 to 3 times faster this way while ma…

Maybe it subjectively feels like 2-3x faster but in studies that measure it we tend to see smaller improvements like in the range of 20-30% faster. It could be that you are an outlier, of course.

2-3x faster on getting the code written. Fully completing a coding task maybe only 20-30% faster, if we count chasing down requirements, reviews, waiting for CI to pass so I can merge etc.

Re: Using LLMs at Oxide

#214

Post by jgalt212 »

minimaxir wrote: post #9
bryancoxwell wrote: post #7

Find it interesting that the section about LLM’s tells when using it for writing is absolutely littered with emdashes

You can stop LLMs from using em-dashes by just telling it to "never use em-dashes". This same type of prompt engineering works to mitigate almost every sign of AI-generated writing, which is one reason why AI writing heuristics/detectors can never be fully reliable.

I guess, but if even in you set aside any obvious tells, pretty much all expository writing out of an LLM still reads like pablum without any real conviction or tons of hedges against observed opinions.

"lack of conviction" would be a useful LLM metric.

Re: Using LLMs at Oxide

#215

Post by rcxdude »

peheje wrote: post #172

I know I'm walking into a den of wolves here and will probably get buried in downvotes, but I have to disagree with the idea that using LLMs for writing breaks some social contract. If you hand me a financial report, I expect you used Excel or a calculator. I don't feel cheated that you didn't do long division by hand to prove your understanding. Writing is no different. The value isn't in how much you sweated while…

I think often, though, people use LLMs as a substitute for thinking about what they want to express in a clear manner. The result is often a large document which locally looks reasonable and well written but overall doesn't communicate a coherant point because there wasn't one expressed to the LLM to begin with, and even a good human writer can only mind-read so much.

Re: Using LLMs at Oxide

#216

Post by CerryuDu »

> LLMs are superlative at reading comprehension, able to process and meaningfully comprehend documents effectively instantly.

I couldn't disagree more. (In fact I'm shocked that Bryan Cantrill uses words like "comprehension" and "meaningfully" in relation to LLMs.)

Summaries provided by ChatGPT, conclusions drawn by it, contain exaggerations and half-truths that are NOT there in the actual original sources, if you bother enough to ask ChatGPT for those, and to read them yourself. If your question is only slightly suggestive, ChatGPT's tuning is all too happy to tilt the summary in your favor; it tells you what you seem to want to hear, based on the phrasing of your prompt. ChatGPT presents, using confident and authoritative language, total falsehoods and deceptive half-truths, after parsing human-written originals, be the latter natural language text, or source code. I now only trust ChatGPT to recommend sources to me, and I read those -- especially the relevant-looking parts -- myself. ChatGPT has been tuned by its masters to be a lying sack of shit.

I've recently asked ChatGPT a factual question: I asked it about the identity of a public figure (an artist) whom I had seen in a video on youtube. ChatGPT answered with "Person X", and even explained why Person X's contribution was so great to the piece of art in question. I knew the answer was wrong, so I retorted only with: "Source?". Then ChatGPT apologized, and did the exact same thing, just with "Person Y"; again explaining why Person Y was so influental in making that piece of art so great. I knew the answer was wrong still, so I again said: "Source?". And at third attempt, ChatGPT finally said "Person Z", with a verifiable reference to a human-written document that identified the artist.

FUCK ChatGPT.

Re: Using LLMs at Oxide

#217

Post by sevensor »

zx8080 wrote: post #72
girvo wrote: post #55

Earlier quoted context omitted.

Watching my juniors constantly fight the nonsense auto completion suggestions their LLM editor of choice put in front of them, or worse watching them accept it and proceed to get entirely lost in the sauce, I’m not entirely convinced that the autocompletion part of it is the best one. Tools like Claude code with ask/plan mode seem to be better in my experience, though I absolutely do wonder about the lack of typing c…

> but I swear it built the comprehension I have today. For interns/junior engineers, the choice is: comprehension VS career. And I won't be surprised if most of them will go with career now, and comprehension.. well thanks maybe tomorrow (or never).

I have worked with a lot of junior engineers, and I’ll take comprehension any day. Developing their comprehension is a huge part of my responsibility to them and to the company. It’s pretty wasteful to take a human being with a functioning brain and ask them to churn out half understood code that works accidentally. I’m going to have to fix that eventually anyway, so why not get ahead of it and have them understand it so they can fix it instead of me?

Re: Using LLMs at Oxide

#218

Post by CerryuDu »

john01dav wrote: post #20

> Wherever LLM-generated code is used, it becomes the responsibility of the engineer. As part of this process of taking responsibility, self-review becomes essential: LLM-generated code should not be reviewed by others if the responsible engineer has not themselves reviewed it. Moreover, once in the loop of peer review, generation should more or less be removed: if code review comments are addressed by wholesale re-g…

How the heck it does not upset your engineering pride and integrity, to limit your own contribution to verifying and touching up machine slop, is beyond me.

You obviously cannot emotionally identify with the code you produce this way; the ownership you might feel towards such code is nowhere near what meticulously hand-written code elicits.

Re: Using LLMs at Oxide

#219

Post by kfajdsl »

menaerus wrote: post #161
phyzome wrote: post #134

Earlier quoted context omitted.

...what a remarkable thread.

Right? If this is really true, that some random folk without compiler engineering experience, implemented a completely new feature in ocaml compiler by prompting the LLM to produce the code for him, then I think it really is remarkable.

It’s one thing for you (yes, you, the user using the tool) to generate code you don’t understand for a side project or one off tool. It’s another thing to expect your code to be upstreamed into a large project and let others take on the maintenance burden, not to mention review code you haven’t even reviewed yourself!

Note: I, myself, am guilty of forking projects, adding some simple feature I need with an LLM quickly because I don’t want to take the time to understand the codebase, and using it personally. I don’t attempt to upstream changes like this and waste maintainers’ time until I actually take the time myself to understand the project, the issue, and the solution.

Re: Using LLMs at Oxide

#220

Post by mac-attack »

averynicepen wrote: post #46
mcqueenjordan wrote: post #28

As usual with Oxide's RFDs, I found myself vigorously head-nodding while reading. Somewhat rarely, I found a part that I found myself disagreeing with: > Unlike prose, however (which really should be handed in a polished form to an LLM to maximize the LLM’s efficacy), LLMs can be quite effective writing code de novo. Don't the same arguments against using LLMs to write one's prose also apply to code? Was this structu…

Writing is an expression of an individual, while code is a tool used to solve a problem or achieve a purpose. The more examples of different types of problems being solved in similar ways present in an LLM's dataset, the better it gets at solving problems. Generally speaking, if it's a solution that works well, it gets used a lot, so "good solutions" become well represented in the dataset. Human expression, however,…

Very well stated.
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